Abstract
A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method. In this study, we utilised one year of Tasmania’s South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW) method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications.
Highlights
Environmental sensor networks offer a significant contribution to our society, and the data collected are crucial for environmental managers to support decisions for the effective use of natural resources
We suggest that the improved inverse distance weighting (IDW) technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications
The improved IDW comes with two major advantages in terms of computational efficiency: (a) the processing time does not increase as the number of known points increase; (b) we can further improve the performance by applying the kd-tree data structure algorithm, reducing the computational time from O(n) to O(log n) [16]
Summary
Environmental sensor networks offer a significant contribution to our society, and the data collected are crucial for environmental managers to support decisions for the effective use of natural resources. The main restriction is the cost to set up comprehensive sensor networks in these environments and in a real-time manner. It is arguable whether it is necessary to set up such costly networks to obtain high quality data; while we could set up the network only at some critical points and apply appropriate interpolation technique to estimate the remaining unmeasured locations. Li and Heap [2] have provided a comprehensive review of the most commonly-used spatial interpolation techniques used in different areas: environment, hydrology, agriculture, forestry and engineering. A list of statistical error measurements to evaluate the interpolation model are provided in their work
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